The present invention generally relates to an improvement in the selection of significant images in a Picture Archiving and Communication Systems (“PACS”). Specifically, the present invention relates to the automatic generation of a set of significant images based on characteristics or patterns in images being examined.
PACS systems connect to medical diagnostic imaging devices and employ an acquisition gateway (between the acquisition device and the PACS), storage and archiving units, display workstations, databases, and sophisticated data processors. These components are integrated together by a communication network and data management system. A PACS has, in general, the overall goals of streamlining health-care operations, facilitating distributed remote examination and diagnosis, and improving patient care.
A typical application of a PACS system is to provide one or more medical images for examination by a medical professional. For example, a PACS system can provide a series of x-ray images to a display workstation where the images are displayed for a radiologist to perform a diagnostic examination. Based on the presentation of these images, the radiologist can provide a diagnosis. For example, the radiologist can diagnose a tumor or lesion in x-ray images of a patient's lungs.
Once the image data has been preprocessed, a user (such as a radiologist) can access the image data from a display workstation. In general, a user may review several images in an imaging study (or a set of related images) in order to determine if any of the images are “significant.” An image may be significant if one or more objects of interest appear in the image and/or these objects of interest appear in a particular location and/or orientation in the image. In other words, a radiologist may be examining a series of images in an imaging study that were obtained over a given time period. These images may be tracking the growth or development of a tumor in a patient anatomy, for example. The radiologist may wish to find all images that display the tumor in a given position, orientation, and/or size. Therefore, the radiologist can manually examine each image in the study to determine if any of the images are significant (that is, include the tumor in a given position, orientation, and/or size). If the radiologist determines that a given image is significant, the image may be electronically “marked” as significant.
An image may be significant if it is important for a diagnosis. For example, in diagnosing a tumor in an abdomen anatomy, of the many images acquired, only a few may indicate the presence of a tumor. These images may be marked as significant.
An image may be marked as significant by associating one or more attributes with data representative of the image. For example, an entry in a database corresponding to a significant image may be associated with one or more attributes that indicate the significance of the image.
A user may examine images with reference to a representative significant image. A representative significant image is an image that includes one or more objects of interest, or patterns, that the user is interested in. For example, a representative significant image may be a baseline image that all other images being examined are compared to. A radiologist may find an image in an imaging study that includes an object of interest or pattern in a given position, orientation and/or size, for example. The radiologist may then manually review all other images in the imaging study to determine if any of the other images include the same object of interest or pattern in the same position, orientation and/or size, for example. In this way, the radiologist manually reviews or examines images to determine if any of them include one or more patterns that match one or more patterns in the representative significant image.
With increasing volumes of examinations and images, a reduction of radiologists and mounting pressures on improved productivity, radiologists are in dire need of reducing the amount of their time spent manually reviewing and examining images for patterns that match one or more patterns in a representative significant image. For example, typical image data sets or imaging studies can include 3000 or more images which take up a considerable amount of time to read or review.
Therefore, a need exists for reducing the amount of time spent by users in reviewing images in one or more imaging studies to mark images as significant and therefore create sets of significant images. Such a need can be met by defining a pattern description that is based on one or more patterns in one or more representative significant images. This pattern description can include identified patterns in mathematical forms, pattern boundaries, and/or pattern characteristics. Using pattern matching or recognition, this pattern description can then be compared to one or more images in order to determine an amount of match between the pattern description and each image. If an amount of match exceeds or matches one or more thresholds, then the image may be automatically selected and marked as significant. This image may then be included in a set or subset of significant images.
Doing so would allow radiologists to more quickly identify images that are significant. Other than initially identifying the reference or representative significant image and the pattern description, no other manual intervention is required to generate a series of significant images. Moreover, as pattern descriptions may be saved for repeated use, users may more easily generate series of significant images from historical significant image pattern descriptions.
The present invention therefore allows for the generation of significant images from a pattern description generated from one or more historical images (that is, one or more representative significant images). This automated significant image generation allows radiologists to quickly generate one or more significant image series from historical and current imaging studies. By reducing the amount of time required for generating a significant image series, users may then spend more time reading and reviewing the significant images.
In addition, pattern descriptions of a prior imaging study can be used to generate significant images for a current imaging study. Such pattern descriptions may be based on a prior imaging study that has one or more of an imaged body part or anatomy, an imaging procedure, and a type of imaging modality in common with the current imaging study. Thus, it can be beneficial to add automatic generation of significant images for a current study based on the pattern descriptions of a previous comparison study.